CN115054200A - Non-contact continuous dynamic intraocular pressure monitoring system - Google Patents
Non-contact continuous dynamic intraocular pressure monitoring system Download PDFInfo
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Abstract
The invention provides a non-contact continuous dynamic intraocular pressure monitoring system, and belongs to the technical field of intraocular pressure monitoring. The system comprises: a data acquisition module configured to: acquiring an eye pulse wave signal; a feature extraction module configured to: extracting time domain characteristics and frequency domain characteristics according to the obtained eye pulse wave signals; an intraocular pressure calculation module configured to: obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model; the invention realizes continuous online non-contact monitoring of the ocular pressure by detecting the waveform change of the ocular pulse wave, extracting the characteristic vector and constructing the coupling model of the pulse wave characteristic and the ocular pressure.
Description
Technical Field
The invention relates to the technical field of intraocular pressure monitoring, in particular to a non-contact continuous dynamic intraocular pressure monitoring system.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The main basis for the clinical diagnosis of glaucoma is pathological increase of intraocular pressure, when the increase of intraocular pressure exceeds the tolerable degree of optic nerve, irreversible nerve damage can occur, so that the visual function is damaged, and once glaucoma develops, no treatment means can be recovered at present. However, almost all glaucoma is preventable, the key measures are early discovery and early treatment, and continuous intraocular pressure monitoring is a key factor for accurate clinical diagnosis and treatment.
The inventor finds that tonometry can be classified into contact type and non-contact type, Goldmann Applanation Tonometer (GAT) is the gold standard of clinical tonometry at present, but there is a risk of corneal injury, infection, etc. when a measuring head contacts and presses the cornea when tonometry is performed; at present all adopt single tonometer to measure the intraocular pressure clinically, need intermittent to carry out a lot of measurements to the patient, need professional doctor to operate professional equipment constantly in the measurement process, still need anesthesia to patient sometimes, make the measurement process loaded down with trivial details complicacy, waste time and energy, especially night interval measurement is difficult to realize almost, leads to clinician can only obtain a small amount of discrete intraocular pressure data, can't implement accurate treatment to the glaucoma patient.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a non-contact continuous dynamic intraocular pressure monitoring system, which realizes continuous online non-contact monitoring of the intraocular pressure by detecting the waveform change of the eye pulse wave, extracting the characteristic vector, and constructing a coupling model of the pulse wave characteristic and the intraocular pressure.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a non-contact continuous dynamic intraocular pressure monitoring system in a first aspect.
A non-contact continuous dynamic intraocular pressure monitoring system comprising:
a data acquisition module configured to: acquiring an eye pulse wave signal;
a feature extraction module configured to: extracting time domain characteristics and frequency domain characteristics according to the obtained eye pulse wave signals;
an intraocular pressure calculation module configured to: and obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model.
As an optional implementation manner, the time domain feature includes: time parameters, amplitude parameters, slope parameters, area parameters, and other parameters of the human body.
As an optional implementation manner, the time parameter includes: one or more of pulse cycle time, main wave rising time, time from a starting point to a trough of a counterpulsation wave, time from a trough of the counterpulsation wave to an end point, main wave peak time at the same height of a counterpulsation wave trough, blood vessel hardness index, time difference of a main wave peak point and a secondary wave peak point of second-order difference, and time difference of the main wave peak point and the counterpulsation wave peak point.
As an optional implementation manner, the amplitude parameter includes: the amplitude difference of the ascending branch, the amplitude difference of the descending branch, the amplitude from the main wave peak to the starting point, the amplitude from the dicrotic wave to the starting point, the peripheral resistance coefficient, the amplitude of the dicrotic wave valley, the ratio of the amplitude difference of the wave peak and the dicrotic wave to the amplitude difference of the wave peak and the wave valley, the ratio of the amplitude of the wave peak to the amplitude of the starting point, the ratio of the amplitude of the wave peak to the amplitude of the wave valley, the height of the peak point of the dicrotic wave, the relative height of the central isthmus and the height of the main peak point of the first-order difference pulse wave signal.
As an optional implementation manner, the slope parameter includes: one or more of a main wave rising branch slope, a main wave falling branch slope, a rebroadcast wave rising branch slope, and a rebroadcast wave falling branch slope.
As an optional implementation manner, the area parameter includes: one or more of main wave ascending branch area, main wave descending branch area, main wave ascending branch to main wave descending branch area ratio, counterpulsation wave ascending branch area, counterpulsation wave descending branch area, and counterpulsation wave area to main wave area ratio.
As an optional implementation manner, the other parameters of the human body at least include: one or more of heart rate, blood oxygen, posture, and individual variability.
As an optional implementation manner, the frequency domain feature includes: cepstral coefficients.
As an optional implementation manner, the intraocular pressure model is obtained by using a machine learning or multiple linear regression method, where the intraocular pressure model includes:
EP＝a×f(x)+b×BP+c×f(y)
wherein EP is intraocular pressure, f (x) is a characteristic function set of an eye vein, BP is calibration blood pressure, f (y) is an individual difference function set, and a, b and c are weight coefficients.
As an optional implementation manner, a photoelectric volume type pulse wave sensor is adopted to obtain an arterial pulse wave signal by measuring an optical signal of blood in an artery of eye tissue of a tested person.
As an optional implementation manner, the eye pulse wave signal acquisition point includes: central retinal artery, short posterior ciliary artery, long posterior ciliary artery, anterior ciliary artery, and choroid layer.
As an optional implementation mode, pulse wave signals are collected at artery points of other parts of the body of the testee and are used for correcting eye pulse wave signals, errors caused by blood pressure changes to intraocular pressure measurement are removed, and an intraocular pressure model is corrected according to individual parameters of the testee.
A second aspect of the present invention provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, performs the steps of:
acquiring an eye pulse wave signal;
extracting time domain characteristics and frequency domain characteristics according to the obtained eye pulse wave signals;
and obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model.
A third aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the following steps:
acquiring an eye pulse wave signal;
extracting time domain characteristics and frequency domain characteristics according to the acquired eye pulse wave signals;
and obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model.
Compared with the prior art, the invention has the beneficial effects that:
1. the non-contact continuous dynamic intraocular pressure monitoring system provided by the invention has the advantages that the waveform change of the eye pulse wave is detected, the characteristic vector is extracted, and the coupling model of the pulse wave characteristic and the intraocular pressure is constructed, so that the continuous online non-contact monitoring of the intraocular pressure is realized, a clinician is assisted to carry out accurate treatment on diseases, and the glaucoma blinding rate is reduced.
2. The non-contact continuous dynamic intraocular pressure monitoring system comprises the following time domain characteristics: time parameters, amplitude parameters, slope parameters, area parameters and other parameters of the human body; the frequency domain features include: the cepstrum coefficient is used by fusing multiple parameters, so that more accurate intraocular pressure monitoring is realized.
3. The non-contact continuous dynamic intraocular pressure monitoring system provided by the invention constructs an intraocular pressure model according to the characteristic function set of the eye veins, the calibration blood pressure and the individual difference function set, and the decompression monitoring precision is further improved by a multi-parameter fusion mode.
4. The non-contact continuous dynamic intraocular pressure monitoring system collects pulse wave signals at artery points of other parts of the body of a measured person, is used for correcting the eye pulse wave signals, removes errors caused by blood pressure changes to intraocular pressure measurement, corrects an intraocular pressure model according to individual parameters of the measured person, ensures the precision of the intraocular pressure model and improves the accuracy of intraocular pressure monitoring.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are included to illustrate an exemplary embodiment of the invention and not to limit the invention.
Fig. 1 is a schematic structural diagram of a non-contact continuous dynamic intraocular pressure monitoring system provided in embodiment 1 of the present invention.
Fig. 2 is a schematic diagram of sampling an eye pulse wave signal according to embodiment 1 of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1:
the eyeball and peripheral tissues contain abundant arteriovenous blood vessel networks, and when the intraocular pressure is increased, the blood vessels are inevitably squeezed to cause the change of the shape of the blood vessels and the internal blood flow, and the change of the pulse waveform characteristics of the blood vessels at the eye is represented in signals. Therefore, the intraocular pressure detection method accurately senses the eye pulse wave signals and extracts key features through an optical method, and adopts a machine learning algorithm or a multiple linear regression method to construct an intraocular pressure detection model based on the eye pulse sensitive features; specifically, in the individual embodiment, the current intraocular pressure is measured in advance, the model is calibrated, and the model parameters are corrected; and then detecting an eye pulse wave signal of the target object by an optical method, extracting required sensitive characteristics from the pulse wave signal, and inputting characteristic values into the established intraocular pressure detection model subjected to calibration and correction to finish intraocular pressure non-invasive measurement. Because the optical method can continuously acquire the eye pulse waveform signal, the established method can realize non-contact continuous dynamic intraocular pressure monitoring.
Specifically, as shown in fig. 1, embodiment 1 of the present invention provides a non-contact continuous dynamic intraocular pressure monitoring system, including:
a data acquisition module configured to: acquiring an eye pulse wave signal;
a feature extraction module configured to: extracting time domain characteristics and frequency domain characteristics according to the obtained eye pulse wave signals;
an intraocular pressure calculation module configured to: and obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model.
Specifically, the data acquisition module includes:
the method comprises the steps of acquiring eye tissue pulse wave signals by adopting various sensors, for example, a photoplethysmography (PPG) sensor can be adopted, and obtaining artery pulse wave signals of a testee by measuring optical signals of blood in an artery of eye tissue; meanwhile, the wearing precision of the acquisition device is monitored by combining the pressure sensor so as to ensure the accuracy of data acquisition.
The eye pulse wave signal acquisition point comprises: the central retinal artery, the short posterior ciliary artery, the long posterior ciliary artery, the anterior ciliary artery, and the choroid layer fix the photoplethysmographic pulse wave sensor and other sensors for assistance to the eye.
Collecting pulse wave signals, filtering noise in the signals, and extracting time domain characteristics and frequency domain characteristics; collecting pulse wave signals at artery points of other parts of the tested person body, and correcting the eye pulse wave signals to remove errors caused by blood pressure changes to intraocular pressure measurement; and acquiring individualized parameters for correcting the intraocular pressure model.
The subset of ocular characteristics, the blood pressure correction value and the individualized parameter set may subsequently be used as inputs, based on a previously established intraocular pressure model, the output value of which is the measured intraocular pressure result.
A feature extraction module comprising:
carrying out difference processing and normalization processing on the acquired original eye pulse wave signals, extracting feature points in a time domain, and obtaining a pulse wave time domain feature parameter set; extracting characteristic points of the original eye pulse wave signals in a frequency domain to obtain a pulse wave frequency domain characteristic parameter set;
during actual measurement, firstly, the eye pulse waveform of a to-be-measured object is obtained, the selected intraocular pressure sensitive characteristic parameters are extracted, the established intraocular pressure model is calibrated, then the obtained sensitive characteristic parameters are input, the intraocular pressure value of the to-be-measured object can be obtained, the eye pulse waveform is obtained in real time, and the intraocular pressure measurement result can be obtained continuously and dynamically in real time.
The ocular pulse wave time domain feature parameter set comprises:
(1) time parameters: one or more of pulse cycle time, main wave rising time, time from a starting point to a trough of a counterpulsation wave, time from a trough of the counterpulsation wave to an end point, main wave peak time at the same height of a counterpulsation wave trough, blood vessel hardness index, time difference of a main wave peak point and a secondary wave peak point of second-order difference, and time difference of the main wave peak point and the counterpulsation wave peak point.
(2) Amplitude parameter: the amplitude difference of ascending branch, the amplitude difference of descending branch, the amplitude from main peak to starting point, the amplitude from counterpulsation wave to starting point, the peripheral resistance coefficient, the amplitude of counterpulsation wave valley, the ratio of the amplitude difference of peak and counterpulsation wave to the amplitude difference of peak and trough, the ratio of peak amplitude to starting point amplitude, the ratio of peak amplitude to trough amplitude, the height of counterpulsation wave peak point, the relative height of descending isthmus and the height of main peak point of first-order difference pulse wave signal.
(3) Slope parameter: one or more of a main wave ascending branch slope, a main wave descending branch slope, a rebroadcast wave ascending branch slope, and a rebroadcast wave descending branch slope.
(4) Area parameters: one or more of main wave ascending branch area, main wave descending branch area, main wave ascending branch and main wave descending branch area ratio, counterpulsation wave ascending branch area, counterpulsation wave descending branch area, counterpulsation wave area and main wave area ratio
(5) Other parameters: one or more of heart rate, blood oxygen, blood pressure, posture, and individual variability.
The eye pulse wave frequency domain feature parameter set comprises cepstrum coefficients.
An intraocular pressure calculation module comprising:
obtaining an intraocular pressure measurement model by using a machine learning or multiple linear regression method:
EP＝a×f(x)+b×BP+c×f(y)
wherein EP is intraocular pressure, f (x) is a characteristic function set of an eye vein, BP is calibration blood pressure, f (y) is an individual difference function set, and a, b and c are weight coefficients.
The intraocular pressure model is calibrated by the existing methods such as pneumatic method, corneal method and extrusion method, the current intraocular pressure is measured and obtained as a calibration value, and the used instrument is an intraocular pressure measuring instrument used clinically.
Specifically, the individual difference function set f (y) described in this embodiment includes a value calculated from the structural parameters of the eye of the subject, such as the lens curvature and the transparency of the aqueous humor, and the value adjusts the intraocular pressure measurement model to reduce the error caused by the difference in the physiological structure of the eye of the subject to the final intraocular pressure measurement. If the value can be: (y) C × Ta, where C is the lens curvature and Ta is the aqueous humor transparency.
It is understood that, in other embodiments, the calculation of the individual difference function set can be performed by those skilled in the art according to other specific physical difference data of other specific subjects, and the selection can be performed according to specific conditions, which is not described herein.
In the present embodiment, examples of two tonometric measurement models are provided as follows:
example 1:
EP＝[(K 1 ×RT 1 -a)+K 2 ×RA]-K 3 ×BP+K 4 ×ID
wherein RT is 1 Is the main wave rise time, RA is the dicrotic wave to origin amplitude, BP is the mean blood pressure value, and ID is the individual parameter value (which is a value derived from prior measurements of the subject's eye physiology).
Example 2:
EP＝[(K 1 ×PTT 1 2 )-K 2 ×SL+DS]-K 3 ×BP+K 4 ×ID
wherein the PTT is 1 Is the time from the start to the trough of the dicrotic wave, SL is the slope of the descending branch of the main wave, DS is the area of the descending branch of the dicrotic wave, BP is the mean blood pressure value, ID is the value of the individual parameter (which is a value obtained by measuring the physiological structure of the subject's eye in advance).
It will be understood that the tonometry model described above is set up according to specific selected parameters, and those skilled in the art are fully capable of performing random combinations to obtain specific test models based on the various time domain parameters and/or frequency domain parameters provided by the present scheme, and are not exhaustive here.
Example 2:
embodiment 2 of the present invention provides a computer-readable storage medium on which a program is stored, the program implementing, when executed by a processor, the steps of:
acquiring an eye pulse wave signal;
extracting time domain characteristics and frequency domain characteristics according to the acquired eye pulse wave signals;
and obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model.
Specifically, the method for acquiring the eye pulse wave signal comprises the following steps:
the pulse wave signals of the eye tissues are acquired by various sensors, for example, a photoplethysmography (PPG) sensor can be used to obtain the arterial pulse wave signals of a testee by measuring the optical signals of blood in the artery of the eye tissues.
The eye pulse wave signal acquisition point comprises: the central retinal artery, the short posterior ciliary artery, the long posterior ciliary artery, the anterior ciliary artery, and the choroid layer, and the photoplethysmographic pulse wave sensor or other sensors are fixed to the eye.
Collecting pulse wave signals, filtering noise in the signals, and extracting time domain characteristics and frequency domain characteristics;
in the embodiment, pulse wave signals are collected at artery points of other parts of the body of the tested person and are used for correcting eye pulse wave signals and removing errors caused by blood pressure changes to intraocular pressure measurement; and acquiring individualized parameters for correcting the intraocular pressure model.
The subset of the characteristics of the eye vessels, the blood pressure correction value and the individualized parameter set can be used as input subsequently, and based on a pre-established intraocular pressure model, the output value of the intraocular pressure model is the intraocular pressure result.
Extracting time domain characteristics and frequency domain characteristics according to the acquired eye pulse wave signals, wherein the steps comprise:
carrying out difference processing and normalization processing on the acquired original eye pulse wave signals, extracting feature points in a time domain, and obtaining a pulse wave time domain feature parameter set; extracting characteristic points of the original eye pulse wave signals in a frequency domain to obtain a pulse wave frequency domain characteristic parameter set;
during actual measurement, firstly, the eye pulse waveform of a to-be-measured object is obtained, the selected intraocular pressure sensitive characteristic parameters are extracted, the established intraocular pressure model is calibrated, then the obtained sensitive characteristic parameters are input, the intraocular pressure value of the to-be-measured object can be obtained, the eye pulse waveform is obtained in real time, and the intraocular pressure measurement result can be obtained continuously and dynamically in real time.
The ocular pulse wave time domain feature parameter set comprises:
(1) time parameters: one or more of pulse cycle time, main wave rising time, time from a starting point to a trough of a counterpulsation wave, time from a trough of the counterpulsation wave to an end point, main wave peak time at the same height of a counterpulsation wave trough, blood vessel hardness index, time difference of a main wave peak point and a secondary wave peak point of second-order difference, and time difference of the main wave peak point and the counterpulsation wave peak point.
(2) Amplitude parameter: the amplitude difference of ascending branch, the amplitude difference of descending branch, the amplitude from main peak to starting point, the amplitude from counterpulsation wave to starting point, the peripheral resistance coefficient, the amplitude of counterpulsation wave valley, the ratio of the amplitude difference of peak and counterpulsation wave to the amplitude difference of peak and trough, the ratio of peak amplitude to starting point amplitude, the ratio of peak amplitude to trough amplitude, the height of counterpulsation wave peak point, the relative height of descending isthmus and the height of main peak point of first-order difference pulse wave signal.
(3) Slope parameter: one or more of a main wave ascending branch slope, a main wave descending branch slope, a rebroadcast wave ascending branch slope, and a rebroadcast wave descending branch slope.
(4) Area parameters are as follows: one or more of main wave ascending branch area, main wave descending branch area, main wave ascending branch to main wave descending branch area ratio, counterpulsation wave ascending branch area, counterpulsation wave descending branch area, and counterpulsation wave area to main wave area ratio
(5) Other parameters: one or more of heart rate, blood oxygen, blood pressure, posture, and individual variability.
The eye pulse wave frequency domain feature parameter set comprises cepstrum coefficients.
Obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model, including:
obtaining an intraocular pressure measurement model by using a machine learning or multiple linear regression method:
EP＝a×f(x)+b×BP+c×f(y)
wherein EP is intraocular pressure, f (x) is a characteristic function set of an eye vein, BP is calibration blood pressure, f (y) is an individual difference function set, and a, b and c are weight coefficients.
The intraocular pressure model is calibrated by the existing methods such as pneumatic method, corneal method and extrusion method, the current intraocular pressure is measured and obtained as a calibration value, and the used instrument is an intraocular pressure measuring instrument used clinically.
Specifically, the individual difference function set f (y) described in this embodiment includes a value calculated from the structural parameters of the eye of the subject, such as the lens curvature and the transparency of the aqueous humor, and the value adjusts the intraocular pressure measurement model to reduce the error caused by the difference in the physiological structure of the eye of the subject to the final intraocular pressure measurement. If the value can be: (y) C × Ta, where C is the lens curvature and Ta is the aqueous humor transparency.
It is understood that, in other embodiments, the calculation of the individual difference function set can be performed by those skilled in the art according to other specific physical difference data of other specific subjects, and the selection can be performed according to specific conditions, which is not described herein.
In the present embodiment, examples of two tonometric measurement models are provided as follows:
example 1:
EP＝[(K 1 ×RT 1 -a)+K 2 ×RA]-K 3 ×BP+K 4 ×ID
wherein RT is 1 Is the main wave rise time, RA is the dicrotic wave to origin amplitude, BP is the mean blood pressure value, and ID is the individual parameter value (which is a value derived from prior measurements of the subject's eye physiology).
Example 2:
EP＝[(K 1 ×PTT 1 2 )-K 2 ×SL+DS]-K 3 ×BP+K 4 ×ID
wherein the PTT is 1 Is the time from the start to the trough of the dicrotic wave, SL is the slope of the descending branch of the main wave, DS is the area of the descending branch of the dicrotic wave, BP is the mean blood pressure value, ID is the value of the individual parameter (which is a value obtained by measuring the physiological structure of the subject's eye in advance).
It will be understood that the tonometry model described above is set up according to specific selected parameters, and those skilled in the art are fully capable of performing random combinations to obtain specific test models based on the various time domain parameters and/or frequency domain parameters provided by the present scheme, and are not exhaustive here.
Example 3:
embodiment 3 of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and capable of running on the processor, where the processor executes the program to implement the following steps:
acquiring an eye pulse wave signal;
extracting time domain characteristics and frequency domain characteristics according to the obtained eye pulse wave signals;
and obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model.
Specifically, the method for acquiring the eye pulse wave signal comprises the following steps:
the pulse wave signals of the eye tissues are acquired by various sensors, for example, a photoplethysmography (PPG) sensor can be used to obtain the arterial pulse wave signals of a testee by measuring the optical signals of blood in the artery of the eye tissues.
The eye pulse wave signal acquisition point comprises: the central retinal artery, the short posterior ciliary artery, the long posterior ciliary artery, the anterior ciliary artery, and the choroid layer, and the photoplethysmographic pulse wave sensor or other sensors are fixed to the eye.
Collecting pulse wave signals, filtering noise in the signals, and extracting time domain characteristics and frequency domain characteristics; collecting pulse wave signals at artery points of other parts of the tested person body, and correcting the eye pulse wave signals to remove errors caused by blood pressure changes to intraocular pressure measurement; and acquiring individualized parameters for correcting the intraocular pressure model.
The subset of the characteristics of the eye vessels, the blood pressure correction value and the individualized parameter set can be used as input subsequently, and the output value is the measured intraocular pressure result based on the intraocular pressure model established in advance.
Extracting time domain characteristics and frequency domain characteristics according to the acquired eye pulse wave signals, wherein the steps comprise:
carrying out difference processing and normalization processing on the acquired original eye pulse wave signals, extracting feature points in a time domain, and obtaining a pulse wave time domain feature parameter set; extracting characteristic points from the original eye pulse wave signals in a frequency domain to obtain a pulse wave frequency domain characteristic parameter set;
during actual measurement, firstly, the eye pulse waveform of a to-be-measured object is obtained, the selected intraocular pressure sensitive characteristic parameter is extracted, the established intraocular pressure model is calibrated, then the obtained intraocular pressure sensitive characteristic parameter is input, the intraocular pressure value of the to-be-measured object is obtained, the eye pulse waveform is obtained in real time, and the intraocular pressure measurement result can be obtained continuously and dynamically in real time.
The ocular pulse wave time domain feature parameter set comprises:
(1) time parameters are as follows: one or more of pulse cycle time, main wave rising time, time from a starting point to a trough of a counterpulsation wave, time from a trough of the counterpulsation wave to an end point, main wave peak time at the same height of a counterpulsation wave trough, blood vessel hardness index, time difference of a main wave peak point and a secondary wave peak point of second-order difference, and time difference of the main wave peak point and the counterpulsation wave peak point.
(2) Amplitude parameter: the amplitude difference of ascending branch, the amplitude difference of descending branch, the amplitude from main peak to starting point, the amplitude from counterpulsation wave to starting point, the peripheral resistance coefficient, the amplitude of counterpulsation wave valley, the ratio of the amplitude difference of peak and counterpulsation wave to the amplitude difference of peak and trough, the ratio of peak amplitude to starting point amplitude, the ratio of peak amplitude to trough amplitude, the height of counterpulsation wave peak point, the relative height of descending isthmus and the height of main peak point of first-order difference pulse wave signal.
(3) Slope parameter: one or more of a main wave ascending branch slope, a main wave descending branch slope, a rebroadcast wave ascending branch slope, and a rebroadcast wave descending branch slope.
(4) Area parameters are as follows: one or more of main wave ascending branch area, main wave descending branch area, main wave ascending branch to main wave descending branch area ratio, counterpulsation wave ascending branch area, counterpulsation wave descending branch area, and counterpulsation wave area to main wave area ratio
(5) Other parameters: one or more of heart rate, blood oxygen, blood pressure, posture, and individual variability.
The eye pulse wave frequency domain feature parameter set comprises cepstrum coefficients.
Obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model, including:
obtaining an intraocular pressure measurement model by using a machine learning or multiple linear regression method:
EP＝a×f(x)+b×BP+c×f(y)
wherein EP is intraocular pressure, f (x) is a characteristic function set of an eye vein, BP is calibration blood pressure, f (y) is an individual difference function set, and a, b and c are weight coefficients.
The intraocular pressure model is calibrated by the existing methods such as pneumatic method, corneal method and extrusion method, the current intraocular pressure is measured and obtained as a calibration value, and the used instrument is an intraocular pressure measuring instrument used clinically.
Specifically, the individual difference function set f (y) described in this embodiment includes a value calculated from the structural parameters of the eye of the subject, such as the lens curvature and the transparency of the aqueous humor, and the value adjusts the intraocular pressure measurement model to reduce the error caused by the difference in the physiological structure of the eye of the subject to the final intraocular pressure measurement. If the value can be: (y) C × Ta, where C is the lens curvature and Ta is the aqueous humor transparency.
It is understood that, in other embodiments, the calculation of the individual difference function set can be performed by those skilled in the art according to other specific physical difference data of other specific subjects, and the selection can be performed according to specific conditions, which is not described herein.
In the present embodiment, examples of two tonometric measurement models are provided as follows:
example 1:
EP＝[(K 1 ×RT 1 -a)+K 2 ×RA]-K 3 ×BP+K 4 ×ID
wherein RT is 1 Is the dominant wave rise time, RA is the dicrotic wave to origin amplitude, BP is the mean blood pressure value, and ID is the individual parameter value (which is a value derived from prior measurements of the subject's eye physiology).
Example 2:
EP＝[(K 1 ×PTT 1 2 )-K 2 ×SL+DS]-K 3 ×BP+K 4 ×ID
wherein the PTT is 1 Is the time from the start to the trough of the dicrotic wave, SL is the slope of the descending branch of the main wave, DS is the area of the descending branch of the dicrotic wave, BP is the mean blood pressure value, ID is the value of the individual parameter (which is a value obtained by measuring the physiological structure of the subject's eye in advance).
It will be understood that the tonometry model described above is set up according to specific selected parameters, and those skilled in the art are fully capable of performing random combinations to obtain specific test models based on the various time domain parameters and/or frequency domain parameters provided by the present scheme, and are not exhaustive here.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A non-contact continuous dynamic intraocular pressure monitoring system is characterized in that:
the method comprises the following steps:
a data acquisition module configured to: acquiring an eye pulse wave signal;
a feature extraction module configured to: extracting time domain characteristics and frequency domain characteristics according to the obtained eye pulse wave signals;
an intraocular pressure calculation module configured to: and obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model.
2. The non-contact continuous dynamic intraocular pressure monitoring system of claim 1, wherein:
time domain features, comprising: time parameters, amplitude parameters, slope parameters, area parameters, and other parameters of the human body.
3. The non-contact continuous dynamic intraocular pressure monitoring system of claim 2, wherein:
a time parameter comprising: one or more of pulse cycle time, main wave rising time, time from a starting point to a trough of a counterpulsation wave, time from a trough of the counterpulsation wave to an end point, main wave peak time at the same height of a counterpulsation wave trough, blood vessel hardness index, time difference of a main wave peak point and a secondary wave peak point of second-order difference, and time difference of the main wave peak point and the counterpulsation wave peak point;
alternatively, the first and second liquid crystal display panels may be,
an amplitude parameter comprising: one or more of ascending branch amplitude difference, descending branch amplitude difference, amplitude from main peak to starting point, amplitude from counterpulsation wave to starting point, peripheral resistance coefficient, amplitude of counterpulsation wave valley, ratio of amplitude difference between peak and counterpulsation wave to amplitude difference between peak and trough, ratio of peak amplitude to starting point amplitude, ratio of peak amplitude to trough amplitude, height of counterpulsation wave peak point, relative height of descending isthmus and height of main peak point of first-order difference pulse wave signal;
alternatively, the first and second liquid crystal display panels may be,
slope parameters including: one or more of a main wave ascending branch slope, a main wave descending branch slope, a replay wave ascending branch slope, and a replay wave descending branch slope;
alternatively, the first and second electrodes may be,
area parameters including: one or more of main wave ascending branch area, main wave descending branch area, main wave ascending branch to main wave descending branch area ratio, counterpulsation wave ascending branch area, counterpulsation wave descending branch area and counterpulsation wave area to main wave area ratio;
alternatively, the first and second liquid crystal display panels may be,
other parameters of the human body, including at least: one or more of heart rate, blood oxygen, posture, and individual variability.
4. The non-contact continuous dynamic intraocular pressure monitoring system of claim 1, wherein:
frequency domain features, including: cepstral coefficients.
5. The non-contact continuous dynamic intraocular pressure monitoring system of claim 1, wherein:
obtaining an intraocular pressure model by adopting a machine learning or multiple linear regression method, wherein the intraocular pressure model comprises the following steps:
EP＝a×f(x)+b×BP+c×f(y)
wherein EP is intraocular pressure, f (x) is a characteristic function set of an eye vein, BP is calibration blood pressure, f (y) is an individual difference function set, and a, b and c are weight coefficients.
6. The non-contact continuous dynamic intraocular pressure monitoring system of claim 1, wherein:
the pulse wave sensor is used to measure the optical signal of blood in the artery of eye tissue of the tested person to obtain the pulse wave signal of artery.
7. The non-contact continuous dynamic intraocular pressure monitoring system of claim 6, wherein:
the eye pulse wave signal acquisition point comprises: central retinal artery, short posterior ciliary artery, long posterior ciliary artery, anterior ciliary artery, and choroid layer.
8. The non-contact continuous dynamic intraocular pressure monitoring system of claim 6, wherein:
pulse wave signals are collected at artery points of other parts of the body of the testee and are used for correcting eye pulse wave signals, errors caused by blood pressure changes to intraocular pressure measurement are removed, and an intraocular pressure model is corrected according to individual parameters of the testee.
9. A computer-readable storage medium having a program stored thereon, the program, when executed by a processor, implementing the steps of:
acquiring an eye pulse wave signal;
extracting time domain characteristics and frequency domain characteristics according to the obtained eye pulse wave signals;
and obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model.
10. An electronic device comprising a memory, a processor, and a program stored on the memory and executable on the processor, wherein the processor implements the following steps when executing the program:
acquiring an eye pulse wave signal;
extracting time domain characteristics and frequency domain characteristics according to the obtained eye pulse wave signals;
and obtaining the intraocular pressure corresponding to the current eye pulse wave signal at least according to the time domain characteristic, the frequency domain characteristic and the pre-trained intraocular pressure model.
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